peige-musashi

peige-musashi's Stars

• CASIA-IVA-Lab/AnomalyGPT

  [AAAI 2024 Oral] AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models

  Language:Python77696

• Sirlanri/Efficientvit

  EfficientViT is a new family of vision models for efficient high-resolution vision.

  2

• YoungSean/NIDS-Net

  NIDS-Net: A unified framework for novel instance detection and segmentation

  Language:Python423

• abc-125/segad

  Segmentation-based Anomaly Detector (SegAD)

  Language:Python391

• liliu-avril/Awesome-Segment-Anything

  This repository is for the first comprehensive survey on Meta AI's Segment Anything Model (SAM).

  78950

• facebookresearch/sam2

  The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

  Language:Jupyter Notebook11.3k971

• test-time-training/ttt-lm-pytorch

  Official PyTorch implementation of Learning to (Learn at Test Time): RNNs with Expressive Hidden States

  Language:Python99656

• IDEA-Research/Grounding-DINO-1.5-API

  API for Grounding DINO 1.5: IDEA Research's Most Capable Open-World Object Detection Model Series

  Language:Python73522

• cnulab/RealNet

  Offical implementation of "RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection (CVPR 2024)"

  Language:Python20513

• ross-tsenov/lily58-wireless-view-zmk-config

  Lily58 Pro Wireless Keyboard Spec and Config

  1

• LiheYoung/Depth-Anything

  [CVPR 2024] Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. Foundation Model for Monocular Depth Estimation

  Language:Python6.8k525

• princeton-nlp/SWE-agent

  SWE-agent takes a GitHub issue and tries to automatically fix it, using GPT-4, or your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges.

  Language:Python13.4k1.3k

• AILab-CVC/YOLO-World

  [CVPR 2024] Real-Time Open-Vocabulary Object Detection

  Language:Python4.5k435

• Deci-AI/data-gradients

  Computer Vision dataset analysis

  Language:Python29032

• ViTAE-Transformer/ViTDet

  Unofficial implementation for [ECCV'22] "Exploring Plain Vision Transformer Backbones for Object Detection"

  Language:Python52746

• mit-han-lab/efficientvit

  EfficientViT is a new family of vision models for efficient high-resolution vision.

  Language:Python1.8k164

• CVHub520/X-AnyLabeling

  Effortless data labeling with AI support from Segment Anything and other awesome models.

  Language:Python3.8k442

• yformer/EfficientSAM

  EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

  Language:Jupyter Notebook2.1k151

• ytongbai/LVM

  Language:Python1.7k54

• DonaldRR/SimpleNet

  Language:Python41963

• frh23333/mepu-owod

  Code Implementation of "Unsupervised Recognition of Unknown Objects for Open-World Object Detection"

  Language:Python252

• M-3LAB/awesome-industrial-anomaly-detection

  Paper list and datasets for industrial image anomaly/defect detection (updating). 工业异常/瑕疵检测论文及数据集检索库(持续更新)。

  1.5k138

• TommyZihao/MMOCR_tutorials

  Jupyter notebook tutorials for MMOCR

  Language:Jupyter Notebook409

• IDEA-Research/OpenSeeD

  [ICCV 2023] Official implementation of the paper "A Simple Framework for Open-Vocabulary Segmentation and Detection"

  Language:Python64239

• IDEA-Research/Grounded-Segment-Anything

  Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

  Language:Jupyter Notebook14.9k1.4k

• UX-Decoder/Semantic-SAM

  [ECCV 2024] Official implementation of the paper "Semantic-SAM: Segment and Recognize Anything at Any Granularity"

  Language:Python2.3k109

• xinyu1205/recognize-anything

  Open-source and strong foundation image recognition models.

  Language:Jupyter Notebook2.8k271

• baaivision/Painter

  Painter & SegGPT Series: Vision Foundation Models from BAAI

  Language:Python2.5k167

• visual-layer/fastdup

  fastdup is a powerful, free tool designed to rapidly generate valuable insights from image and video datasets. It helps enhance the quality of both images and labels, while significantly reducing data operation costs, all with unmatched scalability.

  Language:Python1.6k77

• ChaoningZhang/MobileSAM

  This is the official code for MobileSAM project that makes SAM lightweight for mobile applications and beyond!

  Language:Jupyter Notebook4.7k485